Mobile devices are becoming the primary gateway to the internet for many people. Combining functionalities and data of mobile devices with personal computers, sensor devices, internet service platforms, etc. is a major challenge of interoperability. This can be achieved through numerous, individual and personal information spaces in which entities (e.g., service providers, network operators, publishers, application developers, end users, etc.) can place, share, interact and manipulate (or program devices to automatically perform the planning, interaction and manipulation of) webs of information with their own locally agreed semantics without necessarily conforming to an unobtainable, global whole. In addition to information, the information spaces may be combined with webs of shared and interactive computations or computation spaces so that the devices having connectivity to the computation spaces can have the information in the information space manipulated within the computation space environment and the results delivered to the device, rather than the whole process being performed locally in the device.
It is noted that such computation spaces may consist of connectivity between devices, from devices to network infrastructure, to distributed information spaces so that computations can be executed where enough computational elements are available. These combined information spaces and computation spaces often referred to as computation clouds, are extensions of the ‘Giant Global Graph’ in which one can apply semantics and reasoning at a local level.
In one example, clouds are working spaces respectively embedded with distributed information and computation infrastructures spanned around computers, information appliances, processing devices and sensors that allow people to work efficiently through access to information and computations from computers or other devices. An information space or a computation space can be rendered by the computation devices physically presented as heterogeneous networks (wired and wireless). However, despite the fact that information and computation presented by the respective spaces can be distributed with different granularity, still there are challenges in certain example implementations to achieve scalable high context information processing within such heterogeneous environments. In various implementations, existing media stream containers, such as MPEG 7, have built-in mechanisms for audio, video and metadata processing and harvesting, yet with limited or no mechanisms for transforming, embedding, extracting, and reasoning media metadata. By way of example, MPEG 7 merely supports keyword searches in metadata written in an Extensible Markup Language (XML) and embedded in media streams for content of interest. A client application or the cloud is required to processes the media stream in a XML protocol through a data manipulation layer, a data analysis layer, a data distribution layer, a storage, etc. The expression for querying in terms of the XML tree is complicated because there are generally a large number of ways to correspond the XML maps onto a logical tree, and the query has to be independent of the choice of the XML map.